# weaviate **Repository Path**: windforce1981/weaviate ## Basic Information - **Project Name**: weaviate - **Description**: No description available - **Primary Language**: Go - **License**: BSD-3-Clause - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-01-20 - **Last Updated**: 2026-01-20 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Weaviate Weaviate logo [![GitHub Repo stars](https://img.shields.io/github/stars/weaviate/weaviate?style=social)](https://github.com/weaviate/weaviate) [![Go Reference](https://pkg.go.dev/badge/github.com/weaviate/weaviate.svg)](https://pkg.go.dev/github.com/weaviate/weaviate) [![Build Status](https://github.com/weaviate/weaviate/actions/workflows/.github/workflows/pull_requests.yaml/badge.svg?branch=main)](https://github.com/weaviate/weaviate/actions/workflows/.github/workflows/pull_requests.yaml) [![Go Report Card](https://goreportcard.com/badge/github.com/weaviate/weaviate)](https://goreportcard.com/report/github.com/weaviate/weaviate) [![Coverage Status](https://codecov.io/gh/weaviate/weaviate/branch/main/graph/badge.svg)](https://codecov.io/gh/weaviate/weaviate) [![Slack](https://img.shields.io/badge/slack--channel-blue?logo=slack)](https://weaviate.io/slack) **Weaviate** is an open-source, cloud-native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification. Weaviate supports two approaches to store vectors: automatic vectorization at import using [integrated models](https://docs.weaviate.io/weaviate/model-providers) (OpenAI, Cohere, HuggingFace, and others) or direct import of [pre-computed vector embeddings](https://docs.weaviate.io/weaviate/starter-guides/custom-vectors). Production deployments benefit from built-in multi-tenancy, replication, RBAC authorization, and [many other features](#weaviate-features). To get started quickly, have a look at one of these tutorials: - [Quickstart - Weaviate Cloud](https://docs.weaviate.io/weaviate/quickstart) - [Quickstart - local Docker instance](https://docs.weaviate.io/weaviate/quickstart/local) ## Installation Weaviate offers multiple installation and deployment options: - [Docker](https://docs.weaviate.io/deploy/installation-guides/docker-installation) - [Kubernetes](https://docs.weaviate.io/deploy/installation-guides/k8s-installation) - [Weaviate Cloud](https://console.weaviate.cloud) See the [installation docs](https://docs.weaviate.io/deploy) for more deployment options, such as [AWS](https://docs.weaviate.io/deploy/installation-guides/aws-marketplace) and [GCP](https://docs.weaviate.io/deploy/installation-guides/gcp-marketplace). ## Getting started You can easily start Weaviate and a local vector embedding model with [Docker](https://docs.docker.com/desktop/). Create a `docker-compose.yml` file: ```yml services: weaviate: image: cr.weaviate.io/semitechnologies/weaviate:1.32.2 ports: - "8080:8080" - "50051:50051" environment: ENABLE_MODULES: text2vec-model2vec MODEL2VEC_INFERENCE_API: http://text2vec-model2vec:8080 # A lightweight embedding model that will generate vectors from objects during import text2vec-model2vec: image: cr.weaviate.io/semitechnologies/model2vec-inference:minishlab-potion-base-32M ``` Start Weaviate and the embedding service with: ```bash docker compose up -d ``` Install the Python client (or use another [client library](#client-libraries-and-apis)): ```bash pip install -U weaviate-client ``` The following Python example shows how easy it is to populate a Weaviate database with data, create vector embeddings and perform semantic search: ```python import weaviate from weaviate.classes.config import Configure, DataType, Property # Connect to Weaviate client = weaviate.connect_to_local() # Create a collection client.collections.create( name="Article", properties=[Property(name="content", data_type=DataType.TEXT)], vector_config=Configure.Vectors.text2vec_model2vec(), # Use a vectorizer to generate embeddings during import # vector_config=Configure.Vectors.self_provided() # If you want to import your own pre-generated embeddings ) # Insert objects and generate embeddings articles = client.collections.get("Article") articles.data.insert_many( [ {"content": "Vector databases enable semantic search"}, {"content": "Machine learning models generate embeddings"}, {"content": "Weaviate supports hybrid search capabilities"}, ] ) # Perform semantic search results = articles.query.near_text(query="Search objects by meaning", limit=1) print(results.objects[0]) client.close() ``` This example uses the `Model2Vec` vectorizer, but you can choose any other [embedding model provider](https://docs.weaviate.io/weaviate/model-providers) or [bring your own pre-generated vectors](https://docs.weaviate.io/weaviate/starter-guides/custom-vectors). ## Client libraries and APIs Weaviate provides client libraries for several programming languages: - [Python](https://docs.weaviate.io/weaviate/client-libraries/python) - [JavaScript/TypeScript](https://docs.weaviate.io/weaviate/client-libraries/typescript) - [Java](https://docs.weaviate.io/weaviate/client-libraries/java) - [Go](https://docs.weaviate.io/weaviate/client-libraries/go) - C# (🚧 Coming soon 🚧) There are also additional [community-maintained libraries](https://docs.weaviate.io/weaviate/client-libraries/community). Weaviate exposes [REST API](https://docs.weaviate.io/weaviate/api/rest), [gRPC API](https://docs.weaviate.io/weaviate/api/grpc), and [GraphQL API](https://docs.weaviate.io/weaviate/api/graphql) to communicate with the database server. ## Weaviate features These features enable you to build AI-powered applications: - **⚡ Fast Search Performance**: Perform complex semantic [searches](https://docs.weaviate.io/weaviate/search/similarity) over billions of vectors in milliseconds. Weaviate's architecture is built in Go for speed and reliability, ensuring your AI applications are highly responsive even under heavy load. See our [ANN benchmarks](https://docs.weaviate.io/weaviate/benchmarks/ann) for more info. - **🔌 Flexible Vectorization**: Seamlessly vectorize data at import time with [integrated vectorizers](https://docs.weaviate.io/weaviate/model-providers) from OpenAI, Cohere, HuggingFace, Google, and more. Or you can import [your own vector embeddings](https://docs.weaviate.io/weaviate/starter-guides/custom-vectors). - **🔍 Advanced Hybrid & Image Search**: Combine the power of semantic search with traditional [keyword (BM25) search](https://docs.weaviate.io/weaviate/search/bm25), [image search](https://docs.weaviate.io/weaviate/search/image) and [advanced filtering](https://docs.weaviate.io/weaviate/search/filters) to get the best results with a single API call. - **🤖 Integrated RAG & Reranking**: Go beyond simple retrieval with built-in [generative search (RAG)](https://docs.weaviate.io/weaviate/search/generative) and [reranking](https://docs.weaviate.io/weaviate/search/rerank) capabilities. Power sophisticated Q&A systems, chatbots, and summarizers directly from your database without additional tooling. - **📈 Production-Ready & Scalable**: Weaviate is built for mission-critical applications. Go from rapid prototyping to production at scale with native support for [horizontal scaling](https://docs.weaviate.io/deploy/configuration/horizontal-scaling), [multi-tenancy](https://docs.weaviate.io/weaviate/manage-collections/multi-tenancy), [replication](https://docs.weaviate.io/deploy/configuration/replication), and fine-grained [role-based access control (RBAC)](https://docs.weaviate.io/weaviate/configuration/rbac). - **💰 Cost-Efficient Operations**: Radically lower resource consumption and operational costs with built-in [vector compression](https://docs.weaviate.io/weaviate/configuration/compression). Vector quantization and multi-vector encoding reduce memory usage with minimal impact on search performance. For a complete list of all functionalities, visit the [official Weaviate documentation](https://docs.weaviate.io). ## Useful resources ### Demo projects & recipes These demos are working applications that highlight some of Weaviate's capabilities. Their source code is available on GitHub. - [Elysia](https://elysia.weaviate.io) ([GitHub](https://github.com/weaviate/elysia)): Elysia is a decision tree based agentic system which intelligently decides what tools to use, what results have been obtained, whether it should continue the process or whether its goal has been completed. - [Verba](https://weaviate.io/blog/verba-open-source-rag-app) ([GitHub](https://github.com/weaviate/verba)): A community-driven open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. - [Healthsearch](https://weaviate.io/blog/healthsearch-demo) ([GitHub](https://github.com/weaviate/healthsearch-demo)): An open-source project aimed at showcasing the potential of leveraging user-written reviews and queries to retrieve supplement products based on specific health effects. - Awesome-Moviate ([GitHub](https://github.com/weaviate-tutorials/awesome-moviate)): A movie search and recommendation engine that allows keyword-based (BM25), semantic, and hybrid searches. We also maintain extensive repositories of **Jupyter Notebooks** and **TypeScript code snippets** that cover how to use Weaviate features and integrations: - [Weaviate Python Recipes](https://github.com/weaviate/recipes/) - [Weaviate TypeScript Recipes](https://github.com/weaviate/recipes-ts/) ### Blog posts - [What is a Vector Database](https://weaviate.io/blog/what-is-a-vector-database) - [What is Vector Search](https://weaviate.io/blog/vector-search-explained) - [What is Hybrid Search](https://weaviate.io/blog/hybrid-search-explained) - [How to Choose an Embedding Model](https://weaviate.io/blog/how-to-choose-an-embedding-model) - [What is RAG](https://weaviate.io/blog/introduction-to-rag) - [RAG Evaluation](https://weaviate.io/blog/rag-evaluation) - [Advanced RAG Techniques](https://weaviate.io/blog/advanced-rag) - [What is Multimodal RAG](https://weaviate.io/blog/multimodal-rag) - [What is Agentic RAG](https://weaviate.io/blog/what-is-agentic-rag) - [What is Graph RAG](https://weaviate.io/blog/graph-rag) - [Overview of Late Interaction Models](https://weaviate.io/blog/late-interaction-overview) ### Integrations Weaviate integrates with many external services: | Category | Description | Integrations | | ------------------------------------------------------------------------------------------ | ---------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **[Cloud Hyperscalers](https://docs.weaviate.io/integrations/cloud-hyperscalers)** | Large-scale computing and storage | [AWS](https://docs.weaviate.io/integrations/cloud-hyperscalers/aws), [Google](https://docs.weaviate.io/integrations/cloud-hyperscalers/google) | | **[Compute Infrastructure](https://docs.weaviate.io/integrations/compute-infrastructure)** | Run and scale containerized applications | [Modal](https://docs.weaviate.io/integrations/compute-infrastructure/modal), [Replicate](https://docs.weaviate.io/integrations/compute-infrastructure/replicate), [Replicated](https://docs.weaviate.io/integrations/compute-infrastructure/replicated) | | **[Data Platforms](https://docs.weaviate.io/integrations/data-platforms)** | Data ingestion and web scraping | [Airbyte](https://docs.weaviate.io/integrations/data-platforms/airbyte), [Aryn](https://docs.weaviate.io/integrations/data-platforms/aryn), [Boomi](https://docs.weaviate.io/integrations/data-platforms/boomi), [Box](https://docs.weaviate.io/integrations/data-platforms/box), [Confluent](https://docs.weaviate.io/integrations/data-platforms/confluent), [Astronomer](https://docs.weaviate.io/integrations/data-platforms/astronomer), [Context Data](https://docs.weaviate.io/integrations/data-platforms/context-data), [Databricks](https://docs.weaviate.io/integrations/data-platforms/databricks), [Firecrawl](https://docs.weaviate.io/integrations/data-platforms/firecrawl), [IBM](https://docs.weaviate.io/integrations/data-platforms/ibm), [Unstructured](https://docs.weaviate.io/integrations/data-platforms/unstructured) | | **[LLM and Agent Frameworks](https://docs.weaviate.io/integrations/llm-agent-frameworks)** | Build agents and generative AI applications | [Agno](https://docs.weaviate.io/integrations/llm-agent-frameworks/agno), [Composio](https://docs.weaviate.io/integrations/llm-agent-frameworks/composio), [CrewAI](https://docs.weaviate.io/integrations/llm-agent-frameworks/crewai), [DSPy](https://docs.weaviate.io/integrations/llm-agent-frameworks/dspy), [Dynamiq](https://docs.weaviate.io/integrations/llm-agent-frameworks/dynamiq), [Haystack](https://docs.weaviate.io/integrations/llm-agent-frameworks/haystack), [LangChain](https://docs.weaviate.io/integrations/llm-agent-frameworks/langchain), [LlamaIndex](https://docs.weaviate.io/integrations/llm-agent-frameworks/llamaindex), [N8n](https://docs.weaviate.io/integrations/llm-agent-frameworks/n8n), [Semantic Kernel](https://docs.weaviate.io/integrations/llm-agent-frameworks/semantic-kernel) | | **[Operations](https://docs.weaviate.io/integrations/operations)** | Tools for monitoring and analyzing generative AI workflows | [AIMon](https://docs.weaviate.io/integrations/operations/aimon), [Arize](https://docs.weaviate.io/integrations/operations/arize), [Cleanlab](https://docs.weaviate.io/integrations/operations/cleanlab), [Comet](https://docs.weaviate.io/integrations/operations/comet), [DeepEval](https://docs.weaviate.io/integrations/operations/deepeval), [Langtrace](https://docs.weaviate.io/integrations/operations/langtrace), [LangWatch](https://docs.weaviate.io/integrations/operations/langwatch), [Nomic](https://docs.weaviate.io/integrations/operations/nomic), [Patronus AI](https://docs.weaviate.io/integrations/operations/patronus), [Ragas](https://docs.weaviate.io/integrations/operations/ragas), [TruLens](https://docs.weaviate.io/integrations/operations/trulens), [Weights & Biases](https://docs.weaviate.io/integrations/operations/wandb) | ## Contributing We welcome and appreciate contributions! Please see our [Contributor guide](https://docs.weaviate.io/contributor-guide) for the development setup, code style guidelines, testing requirements and the pull request process. Join our [Slack community](https://weaviate.io/slack) or [Community forum](https://forum.weaviate.io/) to discuss ideas and get help. ## License BSD 3-Clause License. See [LICENSE](./LICENSE) for details.